摘要
本文采用了DenseNet结合迁移学习的分类模型,对胰腺黏液性囊性肿瘤(MCN)和浆液性囊性肿瘤(SCN)进行分类.首先对来自长海医院的65例MCN和107例SCN数据进行扩增和预处理,其次构建DenseNet结合迁移学习的分类模型并进行微调,实验过程采用五折交叉验证,对MCN和SCN进行识别分类,并将该模型与AlexNet、VGG16、ResNet50等其他深度学习模型进行对比.结果显示本文的分类模型识别效果最好,在测试集上ROC曲线下面积(AUC值)达到0.989,准确率为0.943,召回率为0.949,精确率为0.938.由此可见基于DenseNet结合迁移学习的分类模型对MCN和SCN具有较高的识别准确率,优于其他深度学习模型,并具有较强的学习能力,可以辅助医生在临床上的诊断,在一定程度上节省人力物力.该模型对于胰腺囊性肿瘤识别分类的潜在价值和临床意义.
This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor(MCN)from serous cystic tumor(SCN)of the pancreas.Firstly,the data of 65 MCNs and 107 SCNs from Changhai Hospital were augemented and preprocessed.Secondly,the classification model of DenseNet combined with transfer learning was constructed and fine-tuned,MCN and SCN were classified by 5-fold cross validation,and the proposed classification model was compared with AlexNet,VGG16,ResNet50 and other deep learning models.The classification model in this paper yielded the best recognition effect.The area under the ROC curve(AUC value),accuracy rate,recall rate and precision rate of the test set were 0.989,0.943,0.949 and 0.938 respectively.It proved that the classification model based on DenseNet combined with transfer learning has higher recognition accuracy for MCN and SCN and stronger learning ability than other deep learning models,which can help doctors in clinical diagnosis,and save manpower and material resources.It further confirmed the potential value and clinical significance of this model for the classification of pancreatic cystic tumors.
作者
田慧
武杰
边云
张志伟
邵成伟
TIAN Hui;WU Jie;BIAN Yun;ZHANG Zhiwei;SHAO Chengwei(School of Health Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Department of Radiology,The First Affiliated Hospital of Naval Medical University,Shanghai 200434,China)
出处
《波谱学杂志》
CAS
北大核心
2023年第3期270-279,共10页
Chinese Journal of Magnetic Resonance
